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    <title>CUST 机器学习, 2025 秋</title>

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                    <span class="title">CUST 机器学习日程表</span>
                    <h3>医学影像计算工程实验室, 2025 秋</h3>
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        <table class="schedule" align="center" border="1" width="986">
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                    <td align="center" width="103"><strong>时间</strong></td>
                    <td align="center" width="232"><strong>主题</strong></td>
                    <td align="center" width="250"><strong>参考资料</strong></td>
                    <td align="center" width="120"><strong>附件</strong></td>
                    <td align="center" width="120"><strong>报告人</strong></td>
                </tr>

                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 2 周 监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Thurs 09/05/2024</td>
                    <td align="left">线性回归、逻辑回归、K 近邻分类</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                            <li>
                                机器学习 3.2、3.3、3.4、10.1
                            </li>
                            <li>
                                统计学习方法 6.1, 第 3 章
                            </li>
                            <li>
                                <a href="http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/">
                                    UFLDL Tutorial Linear Regression
                                </a>
                            </li>
                            <li>
                                <a href="http://ufldl.stanford.edu/tutorial/supervised/LogisticRegression/">
                                    UFLDL Tutorial Logistic Regression
                                </a>
                            </li>
                            <li>
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/Logistic_and_maximum_entropy_models/logisticRegression.py">
                                    LogisticRegression,
                                </a>
                            <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/KNN/KNN.py">
                                KNN
                            </a>
                            <li>
                                <a href="https://zh-v2.d2l.ai/chapter_linear-networks/linear-regression-scratch.html">
                                    线性回归，
                                </a>
                                <a href="https://zh-v2.d2l.ai/chapter_linear-networks/softmax-regression.html">
                                    Softmax 回归
                                </a>
                            </li>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td></td>
                </tr>

                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 3 周 监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">支持向量机、决策树</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                                <li>
                                    机器学习第 4, 6 章
                                </li>
                                <li>
                                    统计学习方法第 5, 7 章
                                </li>
                                <li>
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/DecisionTree/DecisionTree.py">
                                    决策树
                                </a>
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/SVM/SVM.py">
                                    支持向量机
                                </a>
                                </li>
                                <li>
                                <a href="https://www.bilibili.com/video/BV1jt4y1E7BQ/?spm_id_from=333.337.search-card.all.click&vd_source=721ac4481300b04ca5abec3fd5c3fd0c">
                                    浙大 胡浩基 支持向量机
                                </a>
                                </li>
                                <li>
                                    <a href="https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py">
                                        决策树，
                                    </a>
                                    <a href="https://github.com/TheAlgorithms/Python/blob/master/machine_learning/support_vector_machines.py">
                                        支持向量机
                                    </a>
                                </li>
                        </ol>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td></td>
                </tr>

                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 4 周 监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">朴素贝叶斯、EM 算法</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                            <li>
                                机器学习 7.3, 7.6
                            </li>
                            <li>
                                统计学习方法第 4, 9 章
                            </li>
                            <li>
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/NaiveBayes/NaiveBayes.py">
                                    朴素贝叶斯，
                                </a>
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/EM/EM.py">
                                    EM 算法
                                </a>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td></td>
                </tr>

                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 5 周 监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">隐马尔可夫模型</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                            <li>
                                机器学习 14.1
                            </li>
                            <li>
                                统计学习方法第 10 章
                            </li>
                            <li>
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/HMM/HMM.py">
                                    隐马尔可夫模型
                                </a>
                            </li>
                            <li>
                                <a href="https://github.com/hmmlearn/hmmlearn/blob/main/src/hmmlearn/hmm.py">
                                    hmmlearn
                                </a>
                            </li>
                            
                        </ol>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td></td>
                </tr>

                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 6 周 监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">条件随机场</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                            <li>
                                机器学习 14.2
                            </li>
                            <li>
                                统计学习方法第 11 章
                            </li>
                            <li>
                                <a href="https://github.com/kmkurn/pytorch-crf">
                                    pytorch-crf
                                </a>
                            </li>

                        </ol>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td></td>
                </tr>
            
                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 7 周 无监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">KNN 聚类</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                            <li>
                                机器学习第 9 章
                            </li>
                            <li>
                                统计学习方法第 14 章
                            </li>
                            <li>
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/Clustering/K-means_Clustering/K-means_Clustering.py">
                                    K-means_Clustering
                                </a>
                            </li>
                            <li>
                                <a href="https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py">
                                    K-means_Clustering
                                </a>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td></td>
                </tr>

                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 8 周 无监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">主成分分析、T-SNE</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                            <li>
                                机器学习 12.2
                            </li>
                            <li>
                                统计学习方法第 16 章
                            </li>
                            <li>
                                <a href="http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening/">
                                    PCAWhitening
                                </a>
                            </li>
                            <li>
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/PCA/PCA.py">
                                    PCA
                                </a>
                            </li>
                            <li>
                                <a href="https://github.com/pavlin-policar/openTSNE/blob/master/openTSNE/tsne.py">
                                    tsne
                                </a>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td></td>
                </tr>

                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 9 周 无监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">Contrastive Learning、Masked Image Modeling</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                Oord A, Li Y, Vinyals O. Representation learning with contrastive predictive coding[J]. arXiv preprint arXiv:1807.03748, 2018.
                                <a href="https://arxiv.org/pdf/1807.03748.pdf?fbclid=IwAR2G_jEkb54YSIvN0uY7JbW9kfhogUq9KhKrmHuXPi34KYOE8L5LD1RGPTo">
                                    [PDF]
                                </a><br>
                            </li>
                            <li>
                                He K, Fan H, Wu Y, et al. Momentum contrast for unsupervised visual representation learning[C] CVPR 2020.
                                <a href="https://openaccess.thecvf.com/content_CVPR_2020/papers/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.pdf">
                                    [PDF]
                                </a><br>
                            </li>
                            <li>
                                He K, Chen X, Xie S, et al. Masked autoencoders are scalable vision learners[C] CVPR. 2022.
                                <a href="https://link.zhihu.com/?target=https%3A//arxiv.org/abs/2111.06377">
                                    [PDF]
                                </a><br>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./ssl.ipynb">slides</a></td>
                    <td></td>
                </tr>
        
                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 10 周 现代神经网络</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">线性层、卷积操作、自注意力机制</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                <a href="https://zh-v2.d2l.ai/chapter_convolutional-neural-networks/index.html">
                                    动手学深度学习
                                </a><br>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./basic_layer.ipynb">slides</a></td>
                    <td></td>
                </tr>

                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 11 周 现代神经网络</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">BP 算法</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                <a href="https://www.youtube.com/watch?v=VMj-3S1tku0">
                                    The spelled-out intro to neural networks and backpropagation: building micrograd
                                </a><br>
                            </li>
                            <li>
                                <a href="https://www.youtube.com/watch?v=56WUlMEeAuA">
                                    Automatic Differentiation
                                </a><br>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./bp.ipynb">slides</a></td>
                    <td></td>
                </tr>

                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 12 周 现代神经网络</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">VGG、ResNet、InceptionNet、ReplkNet、Vision Transformer、Swin Transformer</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                D. Zoran and Y. Weiss, From Learning Models of Natural Image Patches to Whole Image
                                Restoration,
                                ICCV 2011
                                <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6126278">
                                    [PDF]
                                </a><br>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./basic_net.ipynb">slides</a></td>
                    <td></td>
                </tr>

                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 13 周 生成式 AI</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">自回归编码器</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                [MIT-6.S978 深度生成模型 2024 年秋季](https://mit-6s978.github.io/schedule.html), https://speech.ee.ntu.edu.tw/~hylee/ml/2023-spring.php
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./auto_encoder.ipynb">slides</a></td>
                    <td></td>
                </tr>

                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 14 周 生成式 AI</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">对抗生成网络</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                [MIT-6.S978 深度生成模型 2024 年秋季](https://mit-6s978.github.io/schedule.html),https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.php
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./gan.ipynb">slides</a></td>
                    <td></td>
                </tr>

                <tr>
                    <td colspan="6" class="schedule_week" align="center" height="28" valign="middle">第 15 周 生成式 AI</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">Tues 09/10/2024</td>
                    <td align="left">扩散模型</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                [MIT-6.S978 深度生成模型 2024 年秋季](https://mit-6s978.github.io/schedule.html)
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./diffusion_model.ipynb">slides</a></td>
                    <td></td>
                </tr>

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